title: Wearable diet monitoring through breathing signal analysis.
abstract: This paper presents the design, system structure and performance for a wireless and wearable diet monitoring system. Food and drink intake can be detected by the way of detecting a person's swallow events. The system works based on the key observation that a person's otherwise continuous breathing process is interrupted by a short apnea when she or he swallows as a part of solid or liquid intake process. We detect the swallows through the difference between normal breathing cycle and breathing cycle with swallows using a wearable chest-belt. Three popular machine learning algorithms have been applied on extracted time and frequency domain features. It is shown that high detection performance can be achieved with only few features.
authors.forename: Bo
authors.initials: B
authors.lastname: Dong
authors.forename: Subir
authors.initials: S
authors.lastname: Biswas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003679:Deglutition / Q000502:physiology*; D004032:Diet; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D015203:Reproducibility of Results; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D059015:Wireless Technology